| SynergyFinder: a web application for analyzing drug combination dose–response matrix data A Ianevski, L He, T Aittokallio, J Tang Bioinformatics 33 (15), 2413-2415, 2017 | 513 | 2017 |
| Methods for high-throughput drug combination screening and synergy scoring L He, E Kulesskiy, J Saarela, L Turunen, K Wennerberg, T Aittokallio, ... Cancer systems biology: methods and protocols, 351-398, 2018 | 193 | 2018 |
| Drug target commons: a community effort to build a consensus knowledge base for drug-target interactions J Tang, B Ravikumar, Z Alam, A Rebane, M Vähä-Koskela, G Peddinti, ... Cell chemical biology 25 (2), 224-229. e2, 2018 | 128 | 2018 |
| Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling EI Andersson, S Pützer, B Yadav, O Dufva, S Khan, L He, L Sellner, ... Leukemia 32 (3), 774-787, 2018 | 90 | 2018 |
| Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients L He, J Tang, EI Andersson, S Timonen, S Koschmieder, K Wennerberg, ... Cancer research 78 (9), 2407-2418, 2018 | 68 | 2018 |
| Elevated expression of S100A8 and S100A9 correlates with resistance to the BCL-2 inhibitor venetoclax in AML R Karjalainen, M Liu, A Kumar, L He, D Malani, A Parsons, M Kontro, ... Leukemia 33 (10), 2548-2553, 2019 | 36 | 2019 |
| Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer J Tang, P Gautam, A Gupta, L He, S Timonen, Y Akimov, W Wang, ... NPJ systems biology and applications 5 (1), 20, 2019 | 31 | 2019 |
| Systematic review of computational methods for drug combination prediction W Kong, G Midena, Y Chen, P Athanasiadis, T Wang, J Rousu, L He, ... Computational and structural biotechnology journal 20, 2807-2814, 2022 | 20 | 2022 |
| Characterizing the quality of insight by interactions: A case study C He, L Micallef, L He, G Peddinti, T Aittokallio, G Jacucci IEEE transactions on visualization and computer graphics 27 (8), 3410-3424, 2020 | 18 | 2020 |
| TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples L He, K Wennerberg, T Aittokallio, J Tang Bioinformatics 31 (11), 1866-1868, 2015 | 17 | 2015 |
| Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer L He, D Bulanova, J Oikkonen, A Häkkinen, K Zhang, S Zheng, W Wang, ... Briefings in bioinformatics 22 (6), bbab272, 2021 | 16 | 2021 |
| An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia W Kong, L He, J Zhu, O Brück, K Porkka, CA Heckman, S Zhu, T Aittokallio Leukemia 36 (10), 2384-2395, 2022 | 10 | 2022 |
| Robust scoring of selective drug responses for patient-tailored therapy selection Y Chen, L He, A Ianevski, P Ayuda-Durán, S Potdar, J Saarela, ... Nature Protocols 19 (1), 60-82, 2024 | 2 | 2024 |
| Combined targeting of BET family proteins and BCL2 Is synergistic in acute myeloid leukemia cells overexpressing S100A8 and S100A9 R Karjalainen, M Liu, A Kumar, A Parsons, L He, DR Malani, M Kontro, ... Blood 132, 2634, 2018 | 2 | 2018 |
| User instructions and tutorials of the synergyfinder package L He, K Wennerberg, T Aittokallio, J Tang | 1 | 2018 |
| Computational tools for high-throughput drug combination screening, synergy scoring and predictive modelling in cancer L He Helsingin yliopisto, 2020 | | 2020 |
| User instructions and tutorial of the timma package L He, K Wennerberg, T Aittokallio, J Tang | | 2015 |
| BMS-387032 EI Andersson, S Putzer, B Yadav, O Dufva, S Khan, L He, L Sellner, ... | | |
